It is very common during medical examinations for medical imaging systems (e.g., ultrasound imaging systems) to be used for the detection and diagnosis of abnormalities associated with anatomical structures (e.g., organs such as the heart). Many times, the images are evaluated by a medical expert (e.g., a physician or medical technician) who is trained to recognize characteristics in the images which could indicate an abnormality associated with the anatomical structure or a healthy anatomical structure.
Because of the advancements in computer technology, most computers can easily process large amounts of data and perform extensive computations that can enhance the quality of the obtained images. Furthermore, image processing can be used as a tool to assist in the analysis of the images. Efficient detection of anatomical structures or objects of interest in an image is an important tool in the further analysis of that structure. Many times abnormalities in the shape of an anatomical structure or changes of such a shape through time (e.g., a beating heart or a breathing lung) indicate a tumor or various diseases (e.g., dilation or ischemia of the heart muscle).
Motion estimation is fundamental to computer vision. Underlying any motion estimation method are two principles; similarity function and spatiotemporal smoothing. Known approaches which use similarity functions for motion estimation can be categorized as (i) intensity-based, (ii) histogram-based, and (iii) application specific. Intensity based similarity functions include sum of square distance (SSD), sum of absolute distance (SAD) and normalized cross correlation (NCC).
Motion estimation is very useful in medical imaging application to identify changes in features of an anatomical structure. Analysis of these features can be used to diagnose the health of the anatomical structure and the identification of diseases. For applications where the observed appearance undergoes complex changes in an application-dependent fashion, motion estimation is challenging due to lacking an appropriate similarity function. Known similarity functions are mostly generic and inadequate for handling complex appearance variations.
For example, consider a stress echocardiographic video (stress echo) which is a series of 2D ultrasound images of the human heart captured after the patient undergoes exercise or takes special medicine. Wall motion analysis is used to characterize the functionality of the heart. More specifically, the motion of the endocardium of the left ventricle (LV) is measured. The LV endocardium presents severe appearance changes over a cardiac cycle due to nonrigid deformation, imaging artifacts like speckle noise and signal dropout, movement of papillary muscle (which is attached to the LV endocardium, but not a part of the wall), respiratory interferences, unnecessary probe movement, etc. When know similarity functions are applied to estimate the motion in the stress echo sequences, they are found to be ineffective. There is a need for a method for generating a discriminative similarity function which can effectively be used to perform motion estimation of anatomical structures in situations where appearance undergoes complex changes.